



'''simple docstring'''



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class 						lowerCamelCase_				(       snake_case_      ):







						@slow
						@require_torch
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''					,     '''prajjwal1/bert-tiny'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      BertTokenizer.from_pretrained('''bert-base-uncased'''					)

													SCREAMING_SNAKE_CASE       :   str         =      bertabert.config.encoder.vocab_size
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tokenizer.sep_token_id
													SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer.cls_token_id
													SCREAMING_SNAKE_CASE       :   str         =      1_28

													SCREAMING_SNAKE_CASE       :   int         =      datasets.load_dataset('''cnn_dailymail'''					,     '''3.0.0'''					,     split='''train[:1%]'''					)
													SCREAMING_SNAKE_CASE       :   str         =      datasets.load_dataset('''cnn_dailymail'''					,     '''3.0.0'''					,     split='''validation[:1%]'''					)

													SCREAMING_SNAKE_CASE       :   int         =      train_dataset.select(range(32					)					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      val_dataset.select(range(16					)					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      4

													def _map_to_encoder_decoder_inputs(lowerCAmelCase__ :   List[str]					):
																				# Tokenizer will automatically set [BOS] <text> [EOS]
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      tokenizer(batch['''article''']					,     padding='''max_length'''					,     truncation=lowerCAmelCase__					,     max_length=5_12					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tokenizer(batch['''highlights''']					,     padding='''max_length'''					,     truncation=lowerCAmelCase__					,     max_length=1_28					)
																				SCREAMING_SNAKE_CASE       :   int         =      inputs.input_ids
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      inputs.attention_mask

																				SCREAMING_SNAKE_CASE       :   Tuple         =      outputs.input_ids
																				SCREAMING_SNAKE_CASE       :   List[str]         =      outputs.input_ids.copy()
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      [
																				    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																				]
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      outputs.attention_mask

																				assert all(len(lowerCAmelCase__					) == 5_12 for x in inputs.input_ids					)
																				assert all(len(lowerCAmelCase__					) == 1_28 for x in outputs.input_ids					)

																				return batch

													def _compute_metrics(lowerCAmelCase__ :   Dict					):
																				SCREAMING_SNAKE_CASE       :   Dict         =      pred.label_ids
																				SCREAMING_SNAKE_CASE       :   int         =      pred.predictions

																				# all unnecessary tokens are removed
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tokenizer.batch_decode(lowerCAmelCase__					,     skip_special_tokens=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tokenizer.batch_decode(lowerCAmelCase__					,     skip_special_tokens=lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   Dict         =      sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowerCAmelCase__					)					)]					) / len(lowerCAmelCase__					)

																				return {"accuracy": accuracy}

													# map train dataset
													SCREAMING_SNAKE_CASE       :   Dict         =      train_dataset.map(
													    _map_to_encoder_decoder_inputs					,     batched=lowerCAmelCase__					,     batch_size=lowerCAmelCase__					,     remove_columns=['''article''', '''highlights''']					,     )
													train_dataset.set_format(
													    type='''torch'''					,     columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,     )

													# same for validation dataset
													SCREAMING_SNAKE_CASE       :   List[str]         =      val_dataset.map(
													    _map_to_encoder_decoder_inputs					,     batched=lowerCAmelCase__					,     batch_size=lowerCAmelCase__					,     remove_columns=['''article''', '''highlights''']					,     )
													val_dataset.set_format(
													    type='''torch'''					,     columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,     )

													SCREAMING_SNAKE_CASE       :   str         =      self.get_auto_remove_tmp_dir()

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      SeqaSeqTrainingArguments(
													    output_dir=lowerCAmelCase__					,     per_device_train_batch_size=lowerCAmelCase__					,     per_device_eval_batch_size=lowerCAmelCase__					,     predict_with_generate=lowerCAmelCase__					,     evaluation_strategy='''steps'''					,     do_train=lowerCAmelCase__					,     do_eval=lowerCAmelCase__					,     warmup_steps=0					,     eval_steps=2					,     logging_steps=2					,     )

													# instantiate trainer
													SCREAMING_SNAKE_CASE       :   List[str]         =      SeqaSeqTrainer(
													    model=lowerCAmelCase__					,     args=lowerCAmelCase__					,     compute_metrics=_compute_metrics					,     train_dataset=lowerCAmelCase__					,     eval_dataset=lowerCAmelCase__					,     tokenizer=lowerCAmelCase__					,     )

													# start training
													trainer.train()









'''simple docstring'''



from math import pow
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		):
							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							SCREAMING_SNAKE_CASE       :   Tuple         =      int(pow(A       ,		A					)					)
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
							return current_sum, solutions_count
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
														raise ValueError(
														    '''Invalid input\n'''
														    '''needed_sum must be between 1 and 1000, power between 2 and 10.'''					)

							return backtrack(A       ,		A       ,		1       ,		0       ,		0					)[1]  # Return the solutions_count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




